--- title: "Owen SF | How competitive is any San Francisco address? — Ownify" description: "Owen SF measures pricing competition for any San Francisco address. Built on 8,351 transactions. The money is mappable, but it does not buy competition. The asking price does." lang: en json-ld: | [ { "@context": "https://schema.org", "@type": "Organization", "@id": "https://ownify.com/#organization", "name": "Ownify", "alternateName": "Ownify Homeownership", "url": "https://ownify.com/", "logo": "https://ownify.com/favicon.png", "description": "Ownify is a fractional homeownership platform that helps first-time homebuyers buy a real home with as little as 2% down by partnering with long-term capital investors. Active in Colorado, North Carolina, and Tennessee.", "foundingDate": "2022", "areaServed": [ { "@type": "State", "name": "Colorado" }, { "@type": "State", "name": "North Carolina" }, { "@type": "State", "name": "Tennessee" } ], "sameAs": [ "https://www.linkedin.com/company/ownify", "https://x.com/ownifyhome", "https://www.facebook.com/ownifyhome", "https://www.instagram.com/ownifyhome", "https://www.youtube.com/@ownify" ], "contactPoint": { "@type": "ContactPoint", "contactType": "customer support", "url": "https://ownify.com/contact", "areaServed": [ "US-CO", "US-NC", "US-TN" ], "availableLanguage": "English" } }, { "@context": "https://schema.org", "@type": "WebSite", "@id": "https://ownify.com/#website", "url": "https://ownify.com/", "name": "Ownify", "publisher": { "@id": "https://ownify.com/#organization" }, "inLanguage": "en-US" }, { "@context": "https://schema.org", "@type": "BreadcrumbList", "itemListElement": [ { "@type": "ListItem", "position": 1, "name": "Home", "item": "https://ownify.com/" }, { "@type": "ListItem", "position": 2, "name": "Tech", "item": "https://ownify.com/tech" }, { "@type": "ListItem", "position": 3, "name": "Owen for San Francisco", "item": "https://ownify.com/tech/owenSF" } ] } ] --- We use essential cookies to make our site work. 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You can change your cookie settings at any time by clicking “Preferences .” Accept [![Ownify](/assets/ownify-logo-color-B9tRH5I9.png)](/) [Homebuyers](/homebuyers) [Investors](/investors) [Agents](/agents) [Markets](/markets) [Research](/research/ownify-university) [Blog](/blog) [About](/about) [Sign in](https://app.ownify.com/auth/magic/request?__hstc=53035703.58af27a3f8fc84364b2eb756d5deda99.1787187896984.1787187896984.1787187896984.1&__hssc=53035703.1.1787187896984&__hsfp=459a5155cbd50c6758f41a978f891916)[Get started](https://app.ownify.com/applications/new?__hstc=53035703.58af27a3f8fc84364b2eb756d5deda99.1787187896984.1787187896984.1787187896984.1&__hssc=53035703.1.1787187896984&__hsfp=459a5155cbd50c6758f41a978f891916) Owen SF · pricing competition intelligence # How hot is the SF housing market , really? We asked our AI home buying agent, Owen, to analyze every transaction in San Francisco over the last 3.5 years. Owen mapped who is buying what, where the money is coming from, and how competitive each sale is. Then we turned all of that into empirical pricing models. Use Owen to win your next transaction. Property lookup ## Look up any San Francisco address Create a data-driven pricing analysis based on San Francisco buyer behavior. Look up San Francisco transactions, 2023 to 2026 **8,351**San Francisco transactions, 2023 to 2026 buyers matched to an employer and industry **5,344**buyers matched to an employer and industry of Marina buyers come from VC, PE or finance, and it sells below value **24%**of Marina buyers come from VC, PE or finance, and it sells below value of homes are bought by tech employees **40%**of homes are bought by tech employees Who is buying San Francisco ## Tech, AI, VC and PE money is buying 50%+ of homes in SF. We classified 5,025 employers across 5,344 buyers. Tech has held just under 40% of identified buyers for three years, steady rather than surging. The genuine movement is underneath it: capital is rotating from operators to investors. And the AI money has not come in yet. ### Buyer share by sector, rolling four quarters Shaded bands are 95% confidence intervals ### Artificial intelligence buyers Volatile quarter to quarter, and trending up, but still a small slice ### Two-year change in buyer share, by industry Percentage-point change in each industry's share of identified buyers Venture capital and private equity is the fastest-growing buyer group in the city, up 1.41 points to 3.8% of buyers. Financial services adds 0.96. Artificial intelligence adds 0.84 to reach 6.6%. On the other side, the operating tech that built the city is receding: internet and e-commerce down 1.32 points, software and IT down 1.27. San Francisco is not being bought by more tech people. It is being bought by the people who fund them. Where that money lands ## Every neighborhood has a buyer profile. They are nothing alike. Aggregate shares hide the real structure. At neighborhood level the concentration is extreme, and it is stable enough to price against. ### Industry concentration by neighborhood Top five neighborhoods per group · dashed line is the citywide rate · neighborhoods with 40+ identified buyers Marina draws 24% of its buyers from venture capital, private equity and finance, nearly two and a half times the citywide rate. Mission Bay is 53.8% tech. AI buyers cluster somewhere else again: Twin Peaks, Potrero Hill and Inner Sunset, all well above the 6.2% citywide share. Which raises the obvious question. If you know where the money is, do you know where the competition is? What each industry pays, and where ## Buying power varies widely. But the most competitive transactions are at the lower end of the pricing range. Industry Buyers Median price Sale vs val. Cash % SFR Owen index Venture Capital & Private Equity 166 $2.12M 98.8 23% 58% 40 Self-Employed / Small Business 76 $1.74M 97.2 40% 62% 46 Artificial Intelligence 329 $1.65M 98.0 26% 61% 47 Legal Services 193 $1.61M 96.9 24% 53% 43 Internet & E-commerce 627 $1.60M 97.6 22% 58% 48 Financial Services 375 $1.60M 97.2 22% 61% 47 Biotechnology & Pharmaceuticals 197 $1.55M 99.2 20% 60% 50 Computer Hardware & Semiconductors 357 $1.55M 97.4 19% 61% 46 Hospital & Health Care 295 $1.55M 97.0 23% 62% 50 Software & IT 743 $1.55M 97.5 22% 57% 46 Automotive 80 $1.51M 98.0 12% 64% 52 Marketing & Advertising 80 $1.51M 99.5 22% 55% 49 Media & Entertainment 144 $1.50M 98.6 26% 65% 55 Higher Education & Research 301 $1.50M 98.7 19% 64% 55 Real Estate 144 $1.47M 97.9 22% 60% 44 Architecture & Design 61 $1.46M 97.7 25% 52% 36 Accounting & Consulting 154 $1.46M 97.3 22% 58% 50 Retail & Consumer Goods 118 $1.42M 98.1 30% 58% 48 Manufacturing & Industrial 66 $1.39M 96.1 23% 53% 47 Food & Beverage 61 $1.36M 98.5 26% 59% 37 Construction & Trades 93 $1.34M 96.6 22% 67% 58 Primary & Secondary Education 85 $1.31M 98.1 22% 66% 63 Nonprofit & Philanthropy 115 $1.30M 97.2 27% 56% 47 Government Administration 189 $1.25M 96.3 16% 65% 66 **VC and PE buyers pay the most by a distance**, a $2.12M median, 29% above the all-buyer figure and $475K clear of the next group. They are also the least likely to be buying the competitive end of the market: their median Owen Index is 39.9, near the bottom of the table. They buy expensive, low-contention property in the wealth corridor. **The inversion is at the other end.** Government Administration buyers post the highest median Owen Index in the dataset (66.0) on the lowest median price ($1.25M). Primary and Secondary Education is second (63.1 on $1.31M). Public sector households are concentrated in precisely the ZIPs where bidding is fiercest relative to valuation: the Excelsior, Visitacion Valley, the outer Sunset. They compete hardest for the cheapest homes, while fund principals compete least for the dearest. VC and PE clusters in the Marina and Pacific Heights (94123, 94115). Government workers concentrate in the Excelsior, 16.4% of them in a single ZIP. AI buyers look like the rest of tech: the Mission, the Castro, and the affordable west. Cash offers ## Cash does not buy a discount. It pays a premium. Everyone in San Francisco believes cash wins. It does not win on price. Across 8,328 sales, adjusted for neighborhood, property type, condition, size, price tier and quarter, cash buyers paid **0.95 points more** relative to fair market value than financed buyers, roughly $14,700 on a median sale. Cash here is not a negotiating instrument. It is what wealthy and institutional buyers use to compete for scarce property, and they pay up to do it. The caveat on this analysis: since we don't have data on lost offers, we can't estimate the odds that a cash bid beats a financed one at the same price. ### What a cash offer is worth, by segment Positive means the cash buyer paid more than a financed buyer for equivalent property; grey bars are intervals that cross zero The only place cash buys a discount is the one place financing might genuinely fall through: below average-condition homes, where it takes 2.2 points off, about $34,700. Seller pricing strategies ## Every listing takes a position. Most take the same one. San Francisco Realtors generally follow the same strategy: underprice the listing by 5 to 15% relative to Fair Market Value to invite a bidding war. Does it work? ### List/Value vs Sale/Value of SF real estate transactions, 2023–2026 Random sample of 1931 transactions 2023-26. Fair Market Value is the AVM prior to sale rescaled so the median sale each quarter lands exactly at par ### Successful Auction · 30% Listing agent and seller underpriced the listing and generated a real bidding war. Priced 13% below FMV and closes 5% above FMV. The strategy is real, but it needs a property that can carry it. Often in Portola, Excelsior, Outer Mission, the most affordable and most overbid properties. WINNER: Seller and Listing Agent ### Feel Good Auction · 44% Most homes in San Francisco. Listing Agent and seller price ~14% below Fair Market Value and the average sale closes at 6% below FMV. Seller thinks they won and Agent can claim "my listing closed 10% above list price". Buyers close ~$77k below what Zillow et al show as FMV. Everyone feels good. Concentrated in the mid-market house neighborhoods where underpricing is simply the convention. WINNER: Listing Agent ### Bold Mover · 21% Priced above value and held it. Priced ~7% above FMV and closes at ~7% above FMV. Often seen in new builds and Mission Bay condos. These are the _weakest_ properties in the study by our index. A confident ask does not require a special home. WINNER: Seller and Listing Agent ### Overreach · 5% This is where no seller or listing agent wants to be. The home was priced too high (4% above FMV) and failed to generate interest, closing at 3% below FMV. This does not happen if the agent counsels the seller appropriately but does occur in poor condition properties. WINNER: Buyer Of the sellers who asked below fair market value, 40% cleared it. Of those who asked above, 79% did. Among agents with five or more verified sales, the underpricing habit is strikingly persistent. Owen allows you to model the pricing strategy of every listing agent in the city and optimize how to bid. Owen ## A pricing agent for any San Francisco address. Everything above is what the market looks like in aggregate. Owen answers it for the one address you care about, before it is listed, in the time it takes to type it. ### Ask an address Type in any San Francisco address. Owen returns a price competitiveness score from 0 to 100, the grade behind it. Quick insight into the market for each individual home, for free. ### Build your Pricing Strategy Whether you're a seller, buyer, or Realtor, the Ownify Pricing Report gives you the data and insight to build a pricing strategy based on empirical evidence. ### Bring the evidence Every number traces to the data behind it, so the pricing conversation stops being an argument about instinct. Shouldn't a $3M transaction be based on science and data rather than sales and banter? Property lookup ## Look up any San Francisco address Create a data-driven pricing analysis based on San Francisco buyer behavior. Look up "How competitive is a 3-bed house in the Sunset right now?" Grade B+. Sunset/Parkside sells at 100.6% of fair value on 254 verified sales, but only 9.8% of its sellers ask above value, the lowest rate in the city. The auction convention is near-universal here, and it lands 48% of listings in the Failed Auction cell. "We were going to come in 8% under to start a bidding war." Expect roughly 5.1% of that back in bidding and 2.9% lost. Against the Sunset median of $1.6m that is about $46,000. Your sale-over-asking headline improves by around 6.5 points while the cheque falls by 3.7. "Does it matter that the buyers here are mostly tech?" No. Buyer industry explains none of the variation in sale price relative to value. Sunset is 37.6% tech, close to the citywide 38.5%, and it would price identically either way. Owen scores an address on what is knowable before listing: neighborhood, property type, condition, age, size, price tier and commute geometry. Market timing, pricing strategy and the agent are deliberately excluded from the score, because those are the things you control. It is a pricing analysis, not an appraisal. ## Stop pricing on convention. Owen SF is available to license for brokerages, agents and lenders working in San Francisco. Bring an address to the first call and we will score it live. Request a licenceScore an address live **About the data.** 8,351 San Francisco residential transactions recorded January 2023 to August 2026. Pricing findings rest on the 1,931 with an asking price verified against the record: the MLS overwrites list price with sale price when a listing closes, so we reconstructed the original ask and validated it at 96.4% exact match. Buyer analysis covers 5,344 buyers matched to 5,025 classified employers. Figures are aggregated to neighborhood and quarter; no individual buyer, seller, agent or address is identified. 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